Enhanced Diffusion in Anisotropic and Non-uniform Geometries

TL;DR

This paper generalizes the theory of enhanced diffusion to complex anisotropic and non-uniform geometries, deriving new scaling laws for diffusion rates that depend on domain anisotropy and regularity.

Contribution

It introduces a generalized framework for anisotropic diffusion enhancement and rigorously derives new scaling laws for passive scalar diffusion in complex geometries.

Findings

Diffusion rate scales as rac{pq}{p+q+2} in anisotropic domains.

Theoretical results validated through stochastic process techniques.

Provides insights into transport phenomena in complex, real-world geometries.

Abstract

Enhanced diffusion, which describes the accelerated spread of passive scalars due to the interaction between advection and molecular diffusion, has been extensively studied in simplified geometries, such as uniform shear and radial flows. However, many real-world applications occur in complex, anisotropic domains where standard assumptions do not hold. This paper extends the theory of enhanced diffusion to anisotropic and non-uniform geometries, where scaling in the \(x\)- and \(y\)-directions follows distinct, power-law relationships, and the velocity field exhibits spatially varying regularity. We define a generalized framework for anisotropic diffusion enhancement and rigorously derive new scaling laws for diffusion rates in these settings. Specifically, we show that in a domain with anisotropic scaling functions \( f(x) \sim |x|^p \) and \( g(y) \sim |y|^q \), the enhanced diffusion rate \( r(\kappa) \) for a passive scalar satisfies \( r(\kappa) = C \kappa^{\frac{pq}{p+q+2}} \), where \( C \) depends on the regularity and scaling properties of the domain but remains independent of the diffusivity \( \kappa \). This theoretical result is validated through detailed proofs leveraging stochastic process techniques and variance estimation. Our findings offer a new perspective on the role of domain geometry and anisotropy in diffusion processes, with potential applications in environmental science, engineering, and beyond. This framework lays the groundwork for future studies in both theoretical and applied settings, enabling a deeper understanding of transport phenomena in complex domains.